A Feature-Driven Framework for Software Fault Prediction
Software fault prediction (SFP) is a critical task in software engineering, enabling early identification of faults in modules to improve software quality and reduce maintenance costs. This research investigates the combined effects of feature selection and parameter tuning on the performance of machine learning (ML) models for SFP. This study evaluates the interaction between feature selection methods, including correlation-based feature selection (CFS), recursive feature elimination (RFE), mutual information (MI), and L1 regularization, where hyperparameter tuning techniques such as grid search, randomized search, and genetic algorithm (GA) are used for optimization of ML algorithms, in...
| Authors | Ahmad Nauman Ghazi, Nagajyothi Devarapalli, Ashir Javeed et al. |
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| Primary category | cs.SE |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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